# Domain Adaptation for Code Model-based Unit Test Case Generation

arXiv: https://arxiv.org/abs/2308.08033 · HTML: https://mlsanity.com/paper/2308.08033/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 15 Aug 2023.

## Builds on (most cited first, 15 of 15)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [CodeBLEU: a Method for Automatic Evaluation of Code Synthesis](https://mlsanity.com/paper/2009.10297/) (2020)
- [A Brief Review of Domain Adaptation](https://mlsanity.com/paper/2010.03978/) (2020)
- [An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation](https://mlsanity.com/paper/2302.06527/) (2023)
- [Unit Test Case Generation with Transformers and Focal Context](https://mlsanity.com/paper/2009.05617/) (2020)
- [No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation](https://mlsanity.com/paper/2305.04207/) (2023)
- [TOGA: A Neural Method for Test Oracle Generation](https://mlsanity.com/paper/2109.09262/) (2021)
- [On Learning Meaningful Assert Statements for Unit Test Cases](https://mlsanity.com/paper/2002.05800/) (2020)
- [Learning Deep Semantics for Test Completion](https://mlsanity.com/paper/2302.10166/) (2023)
- [Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers](https://mlsanity.com/paper/2009.05634/) (2020)
- [Methods2Test: A dataset of focal methods mapped to test cases](https://mlsanity.com/paper/2203.12776/) (2022)
- [Improving Automated Program Repair with Domain Adaptation](https://mlsanity.com/paper/2212.11414/) (2022)

## Built on by (most cited first, 7 of 7)

- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [Hallucination to Consensus: Multi-Agent LLMs for End-to-End JUnit Test Generation](https://mlsanity.com/paper/2506.02943/) (2025)
- [Retrieval-Augmented Test Generation: How Far Are We?](https://mlsanity.com/paper/2409.12682/) (2024)
- [Nexus: Execution-Grounded Multi-Agent Test Oracle Synthesis](https://mlsanity.com/paper/2510.26423/) (2025)
- [Generative transformations and patterns in LLM-native approaches for software verification and falsification](https://mlsanity.com/paper/2404.09384/) (2024)
- [Compiling Large Multi-Modal Requirement Documents into Runnable Software Systems: From an Agentic Test-Driven Perspective](https://mlsanity.com/paper/2602.13723/) (2026)
- [Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Tests](https://mlsanity.com/paper/2607.22883/) (2026)

## Abstract (arXiv)

Recently, deep learning-based test case generation approaches have been proposed to automate the generation of unit test cases. In this study, we leverage Transformer-based code models to generate unit tests with the help of Domain Adaptation (DA) at a project level. Specifically, we use CodeT5, a relatively small language model trained on source code data, and fine-tune it on the test generation task. Then, we apply domain adaptation to each target project data to learn project-specific knowledge (project-level DA). We use the Methods2test dataset to fine-tune CodeT5 for the test generation task and the Defects4j dataset for project-level domain adaptation and evaluation. We compare our approach with (a) CodeT5 fine-tuned on the test generation without DA, (b) the A3Test tool, and (c) GPT-4 on five projects from the Defects4j dataset. The results show that tests generated using DA can increase the line coverage by 18.62%, 19.88%, and 18.02% and mutation score by 16.45%, 16.01%, and 12.99% compared to the above (a), (b), and (c) baselines, respectively. The overall results show consistent improvements in metrics such as parse rate, compile rate, BLEU, and CodeBLEU. In addition, we show that our approach can be seen as a complementary solution alongside existing search-based test generation tools such as EvoSuite, to increase the overall coverage and mutation scores with an average of 34.42% and 6.8%, for line coverage and mutation score, respectively.